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CEViNEdit: improving the process of creating cognitively effective graphical editors with GMF

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Abstract

The rise of domain-specific (Visual) languages and the inherent complexity of developing graphical editors for these languages have led to the emergence of proposals that provide support for this task. Most of these proposals are principally based on EMF and GMF, which effectively help to simplify and increase the level of automation of the development process of the editors, but it is important to recall that these proposals have some important disadvantages, mainly related to the learning curve of these technologies, poor documentation or the complexity of providing all the customisation possibilities to the user. In addition, in the process of developing a domain-specific language, issues related to graphical conventions have historically been undervalued, while most of the effort has been focused on semantic aspects. In fact, definitions of the concrete (visual) syntax of modelling languages in Software Engineering are usually based on common sense, intuition, the reuse of existing notations or emulation of common practices. In order to alleviate the inherent complexity of the EMF/GMF approach for the development of graphical editors and to support the evaluation of the quality of visual notations of modelling languages, this article presents CEViNEdit, an intuitive tool that simultaneously supports the semi-automatic generation of graphical editors and the assessment of the cognitive effectiveness of the visual notation implemented by the editor.

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Notes

  1. EuGENia GMF Tutorial: https://www.eclipse.org/epsilon/doc/articles/eugenia-gmf-tutorial/.

  2. http://www.kybele.es/kybdele/.

  3. Generation of GMF Graphical Editors for the Entity—Relationship Model: https://www.kybele.es/cevinedit/EditorsPractice.pdf.

  4. First questionnaire—Knowledge and Preferences: https://www.kybele.es/cevinedit/Q1.pdf.

  5. Graphical editor development with GMF (Spanish): https://www.kybele.es/cevinedit/GMFTutorial.pdf.

  6. Dataset DOI: http://dx.doi.org/10.17632/ddcrpmrhbb.1#file-bdc524f5-e3b6-4f53-aca8-0c9ef69f2f0e.

  7. Second questionnaire—Tools Evaluation: https://www.kybele.es/cevinedit/Q2.pdf.

  8. Members of the Kybele research group: https://www.kybele.es/es/miembros/.

  9. Cognitively Effective Visual Notations: https://www.kybele.es/cevinedit/VNPrinciples.pdf.

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Acknowledgements

This research has been partially funded by the Regional Government of Madrid under the FORTE-CM project (S2018/TCS-4314) and the MADRID (TIN2017-88557-R) and MINECO/FEDER FAME (RTI2018-093608-B-C31) projects, financed by the Spanish Ministry of Economy, Industry and Competitiveness.

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Authors and Affiliations

  1. Kybele Research Group, Rey Juan Carlos University, Calle Tulipán S/N, 28933, Móstoles, Madrid, Spain

    David Granada, Juan M. Vara & Esperanza Marcos

  2. Faculty of Computer Science, Complutense University of Madrid, Ciudad Universitaria, 28040, Madrid, Spain

    Mercedes Merayo

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  1. David Granada
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  2. Juan M. Vara
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  3. Mercedes Merayo
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Correspondence to Juan M. Vara.

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Communicated by Richard Freeman Paige.

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Granada, D., Vara, J.M., Merayo, M. et al. CEViNEdit: improving the process of creating cognitively effective graphical editors with GMF. Softw Syst Model 20, 867–895 (2021). https://doi.org/10.1007/s10270-020-00833-2

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